Monitoring of physical activity in populations - current status and future possibilities

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• Soren Brage
• Monday 17 November 2008, 14:00-15:00
• FW26, William Gates Building.

If you have a question about this talk, please contact Andrew Rice.

Physical activity in large-scale epidemiological studies has traditionally been assessed using subjective instruments such as self-report or proxy-report by teacher/parent for young children. Such assessment is inherently imprecise, as not all activity is committed to memory, and recall of activity is influenced by social desireability. For those reasons, objective methods are increasingly being employed for assessment of activity, with the most common method being accelerometry from a single sensor placed on the waist. Other methods include heart rate monitoring and combined heart rate and movement sensing. Sincehabitual physical activity is a latent variable and therefore in principle not possible to measure, the standard approach is to sample a sufficient number of days to make an inference about the latent activity level. Although not extensively evaluated in the literature, it is generally agreed that 5-7 days of objective monitoring is a fair compromise between feasibility and time sample representativeness. This is, however, still a long time to ask members of the general population to be monitored during their normal daily lives, considering these are usually unpaid volunteers who contribute to medical science for altruistic reasons. With this in mind, feasibility of a method is an absolute priority, since people simply will not wear a monitor if it bothers them too much. To date, therefore, physical activity information has been collected in small tolerable sensors, which because of memory and battery restrictions in a small form factor design is collapsed on-the-fly, resulting in for example a minute-by-minute record of mean magnitude of acceleration or heart rate. Often, filtering and feature extraction algorithms describing the route from raw measurement to stored result are proprietary but nonetheless attempts have been made to infer activity energy expenditure or other phenotypes from this collapsed time-series. With the advent of new technologies capable of storing larger amounts of data over longer periods, however, a more transparent methodology with greater inference potential emerges, which should aid comparability between studies and increase our understanding of the role of physical activity in primary, secondary, and tertiary prevention of disease.

This talk is part of the Computer Laboratory Digital Technology Group (DTG) Meetings series.

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